Tootfinder

Constructing Evidence-Based Tailoring Variables for Adaptive Interventions
John J. Dziak, Inbal Nahum-Shani
https://arxiv.org/abs/2506.03054 https://

Constructing Evidence-Based Tailoring Variables for Adaptive Interventions
Background: An adaptive intervention (ADI) uses individual information in order to select treatment, to improve effectiveness while reducing cost and burden. ADIs require tailoring variables: person- and potentially time-specific information used to decide whether and how to deliver treatment. Specifying a tailoring variable for an intervention requires specifying what to measure, when to measure it, when to make the resulting decisions, and what cutoffs should be used in making those decisions…

MythTriage: Scalable Detection of Opioid Use Disorder Myths on a Video-Sharing Platform
Hayoung Jung, Shravika Mittal, Ananya Aatreya, Navreet Kaur, Munmun De Choudhury, Tanushree Mitra
https://arxiv.org/abs/2506.00308

MythTriage: Scalable Detection of Opioid Use Disorder Myths on a Video-Sharing Platform
Understanding the prevalence of misinformation in health topics online can inform public health policies and interventions. However, measuring such misinformation at scale remains a challenge, particularly for high-stakes but understudied topics like opioid-use disorder (OUD)--a leading cause of death in the U.S. We present the first large-scale study of OUD-related myths on YouTube, a widely-used platform for health information. With clinical experts, we validate 8 pervasive myths and release …

SLURM Heterogeneous Jobs for Hybrid Classical-Quantum Workflows
Aniello Esposito, Utz-Uwe Haus
https://arxiv.org/abs/2506.03846 https://

SLURM Heterogeneous Jobs for Hybrid Classical-Quantum Workflows
A method for efficient scheduling of hybrid classical-quantum workflows is presented, based on standard tools available on common supercomputer systems. Moderate interventions by the user are required, such as splitting a monolithic workflow in to basic building blocks and ensuring the data flow. This bares the potential to significantly reduce idle time of the quantum resource as well as overall wall time of co-scheduled workflows. Relevant pseudo-code samples and scripts are provided to demon…

Strengthening Cybersecurity Resilience in Agriculture Through Educational Interventions: A Case Study of the Ponca Tribe of Nebraska
George Grispos, Logan Mears, Larry Loucks
https://arxiv.org/abs/2505.23800

Strengthening Cybersecurity Resilience in Agriculture Through Educational Interventions: A Case Study of the Ponca Tribe of Nebraska
The increasing digitization of agricultural operations has introduced new cybersecurity challenges for the farming community. This paper introduces an educational intervention called Cybersecurity Improvement Initiative for Agriculture (CIIA), which aims to strengthen cybersecurity awareness and resilience among farmers and food producers. Using a case study that focuses on farmers from the Ponca Tribe of Nebraska, the research evaluates pre- and post- intervention survey data to assess partici…

Minimal Pair-Based Evaluation of Code-Switching
Igor Sterner, Simone Teufel
https://arxiv.org/abs/2506.01840 https://arxiv.org/pdf/25…

Minimal Pair-Based Evaluation of Code-Switching
There is a lack of an evaluation methodology that estimates the extent to which large language models (LLMs) use code-switching (CS) in the same way as bilinguals. Existing methods do not have wide language coverage, fail to account for the diverse range of CS phenomena, or do not scale. We propose an intervention based on minimal pairs of CS. Each minimal pair contains one naturally occurring CS sentence and one minimally manipulated variant. We collect up to 1,000 such pairs each for 11 langu…

Recover Experimental Data with Selection Bias using Counterfactual Logic
Jingyang He, Shuai Wang, Ang Li
https://arxiv.org/abs/2506.00335 https://

Recover Experimental Data with Selection Bias using Counterfactual Logic
Selection bias, arising from the systematic inclusion or exclusion of certain samples, poses a significant challenge to the validity of causal inference. While Bareinboim et al. introduced methods for recovering unbiased observational and interventional distributions from biased data using partial external information, the complexity of the backdoor adjustment and the method's strong reliance on observational data limit its applicability in many practical settings. In this paper, we formally di…

Life Sequence Transformer: Generative Modelling for Counterfactual Simulation
Alberto Cabezas, Carlotta Montorsi
https://arxiv.org/abs/2506.01874 https://

Life Sequence Transformer: Generative Modelling for Counterfactual Simulation
Social sciences rely on counterfactual analysis using surveys and administrative data, generally depending on strong assumptions or the existence of suitable control groups, to evaluate policy interventions and estimate causal effects. We propose a novel approach that leverages the Transformer architecture to simulate counterfactual life trajectories from large-scale administrative records. Our contributions are: the design of a novel encoding method that transforms longitudinal administrative …

This https://arxiv.org/abs/2504.11918 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Deep learning to improve the discovery of near-Earth asteroids in the Zwicky Transient Facility
We present a novel pipeline that uses a convolutional neural network (CNN) to improve the detection capability of near-Earth asteroids (NEAs) in the context of planetary defense. Our work aims to minimize the dependency on human intervention of the current approach adopted by the Zwicky Transient Facility (ZTF). The target NEAs have a high proper motion of up to tens of degrees per day and thus appear as streaks of light in the images. We trained our CNNs to detect these streaks using three dat…

Epidemic dynamics in physical-information-social multilayer networks
Mengshou Wang, Liangrong Peng, Baoguo Jia, Liu Hong
https://arxiv.org/abs/2506.00104 h…

Epidemic dynamics in physical-information-social multilayer networks
During epidemic outbreaks, information dissemination enhances individual protection, while social institutions influence the transmission through measures like government interventions, media campaigns, and hospital resource allocation. Here we develop a tripartite physical-information-social epidemic model and derive the corresponding kinetic equations in different scales by using the Microscopic Markov Chain Approach and mean-field approximations. The basic reproduction number and epidemic th…

Generalizing causal effects with noncompliance: Application to deep canvassing experiments
Zhongren Chen, Melody Huang
https://arxiv.org/abs/2506.00149 htt…

Generalizing causal effects with noncompliance: Application to deep canvassing experiments
Standard approaches in generalizability often focus on generalizing the intent-to-treat (ITT). However, in practice, a more policy-relevant quantity is the generalized impact of an intervention across compliers. While instrumental variable (IV) methods are commonly used to estimate the complier average causal effect (CACE) within samples, standard approaches cannot be applied to a target population with a different distribution from the study sample. This paper makes several key contributions. …